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README.md
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3. Make sure to create necessary directories:
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```
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mkdir -p logs output
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```
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4. Download checkpoint for depth estimator model:
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```
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wget https://ommer-lab.com/files/depthfm/depthfm-v1.ckpt -P external_models/depth-fm/checkpoints/
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```
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5. Run the `SoundingStreet` demo:
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```
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python main.py --panoramic --location "52.3436723,4.8529625"
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```
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Intermediate files such as the downloaded panoramic image and perspective cut-outs can be found in `./logs/`, and output audios for each view as well as the composite audio for the location are saved as `./output/panoramic_composition.wav`
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## Acknowledgements
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- **InternVL2.5-8B-MPO**
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For vision-language modeling, we employ InternVL2.5-8B-MPO, which is released under the MIT License.
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GitHub: https://github.com/OpenGVLab/InternVL
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- **Grounding DINO**
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We use Grounding DINO for open-set object detection. Grounding DINO is released under the Apache 2.0 License.
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GitHub: https://github.com/IDEA-Research/GroundingDINO
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- **DepthFM**
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We utilize the DepthFM model for monocular depth estimation. DepthFM is released under the MIT License.
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GitHub: https://github.com/CompVis/depth-fm
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- **TangoFlux**
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We incorporate TangoFlux for text-to-audio generation. TangoFlux is available for non-commercial research use only and is subject to the Stability AI Community License, WavCaps license, and the original licenses of the datasets used in training.
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GitHub: https://github.com/declare-lab/TangoFlux
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Our repository's license and usage terms adhere to the respective licenses of these models.
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---
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title: SoundingStreet
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emoji: 🚀
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colorFrom: blue
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colorTo: pink
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sdk: gradio
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sdk_version: 5.21.0
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app_file: app.py
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pinned: false
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license: mit
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short_description: Sound Generation
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---
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